Lead Data Scientist

We are actively building AI-driven applications that streamline customer workflows, focusing on business onboarding. With our proprietary identity data assets and deep domain expertise, we are uniquely positioned to expand into a broader set of AI-powered solutions that drive long-term growth. We’re looking for a hands-on applied ML expert to help build the technical foundation for these efforts. Ideally you have shipped external-facing models in the risk/fraud space and know the messy realities of imbalanced data, low labels, and changing behavior. This is a highly technical, hands-on role with wide influence on how we design, build, and scale ML at Middesk.

Responsibilities

Build risk & fraud ML applications: Deliver production ML models in fraud, trust & safety, KYB, and compliance domains, with measurable impact on customer workflows; Tackle hard data problems: Work on classification problems with extreme class imbalance, sparse signals, and “cold start” label challenges; Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and AI agents to improve signal extraction and automate labeling process; Establish ML infrastructure foundations: Partner with the ML infra team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases; Design and implement knowledge graph solutions: Leveraging LLMs for graph construction, querying, and retrieval to enhance entity resolution and business identity use cases

Requirements

Hands-on applied ML expert; 7+ years of production ML experience in one or more of the following areas: Building Production ML for risk, fraud, credit, or trust & safety; Knowledge graph applications; Entity resolution for business or individual identities; Expertise in classification with real-world ML challenges, for example: imbalanced labels, sparse signals, cold start, and production version management; Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines; Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices; Candidates should be based within a commutable distance to the SF/NYC office

Nice to have

B2B SaaS experience, ideally building ML products for enterprise customers; ML pipeline and automation engineering: Experience building end-to-end training harnesses that automate feature engineering, data validation, and model training; Experience scaling ML across multiple products or risk domains